For ease of comparison, we use the same evaluation setup used in [1]} to evaluate the performance of the recommendation algorithms. To this end, we set aside \(80\%\)  of the Book-Crossing dataset as training set and the remaining 20% as the test set. We further extended the prior study of [1]} by incorporating a more comprehensive range of state-of-the-art algorithms, including
(i) baseline approaches, (ii) K-Nearest Neighbour (KNN) approaches, (iii) Matrix Factorization (MF) approaches, and (iv) Neural Network (NN) approaches. Specifically, we evaluate two baselines approaches, i.e., Random and MostPop. We include the UserKNN [3]}, which is a KNN-based method. We also evaluate five MF-based approaches, including MF [4]}, PMF [5]}, NMF [6]}, WMF [7]}, and PF [8]}. We also consider BPR [9]} as one of the well-know ranking-based algorithms. Eventually, we include two state-of-the-art NN-based approaches, i.e., NeuMF [10]} and VAECF [11]}. We adopt the recommendation algorithm with the default hyperparameter settings suggested in their original paper.
